About MovieQA

We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The data set consists of almost 15,000 multiple choice question answers obtained from over 400 movies and features high semantic diversity.

Each question comes with a set of five highly plausible answers; only one of which is correct. The questions can be answered using multiple sources of information: movie clips, plots, subtitles, and for a subset scripts and DVS. Click here to see examples of the data set.

Cite this paper if you use the data:
@inproceedings{MovieQA,
author = {Makarand Tapaswi and Yukun Zhu and Rainer Stiefelhagen and Antonio Torralba and Raquel Urtasun and Sanja Fidler},
title = {MovieQA: Understanding Stories in Movies through Question-Answering},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016}
}
University of Toronto Karlsruhe Institute of Technology Massachusetts Institute of Technology

Changelog

  • 2016.09.01: Evaluation benchmark and leaderboard are open.
  • 2016.03.30: v1.0 data release! Open for public registrations
  • 2016.02.01: v1.0 beta release coming today. Register and help us fix some bugs
  • 2015.12.04: Hello world! The website goes online